Classification of Human Posture from Radar Returns Using Ultra-Wideband Radar

Baird, Zachary; Rajan, Sreeraman; Bolić, Miodrag

doi:10.1109/embc.2018.8513094

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…The system of this kind has a potential to classify posture, activities, detect falls, estimate breathing rate and heart rate and further learn about behavior and patterns of monitored subjects. We worked on several of these applications in our previous works using radars: fall detection (Sadreazami et al, 2019 ), activities (Valdes et al, 2018 ), and classification of posture (Baird et al, 2018 ). All of these works, as well as majority of other published works, focused on a single subject monitoring.…”

Section: Discussionmentioning

confidence: 99%

A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars

Han

Iglesias

et al. 2022

Front. Physiol.

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Respiration rate (RR) and respiration patterns (RP) are considered early indicators of physiological conditions and cardiorespiratory diseases. In this study, we addressed the problem of contactless estimation of RR and classification of RP of one person or two persons in a confined space under realistic conditions. We used three impulse radio ultrawideband (IR-UWB) radars and a 3D depth camera (Kinect) to avoid any blind spot in the room and to ensure that at least one of the radars covers the monitored subjects. This article proposes a subject localization and radar selection algorithm using a Kinect camera to allow the measurement of the respiration of multiple people placed at random locations. Several different experiments were conducted to verify the algorithms proposed in this work. The mean absolute error (MAE) between the estimated RR and reference RR of one-subject and two-subjects RR estimation are 0.61±0.53 breaths/min and 0.68±0.24 breaths/min, respectively. A respiratory pattern classification algorithm combining feature-based random forest classifier and pattern discrimination algorithm was developed to classify different respiration patterns including eupnea, Cheyne-Stokes respiration, Kussmaul respiration and apnea. The overall classification accuracy of 90% was achieved on a test dataset. Finally, a real-time system showing RR and RP classification on a graphical user interface (GUI) was implemented for monitoring two subjects.

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Section: Discussionmentioning

confidence: 99%

A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars

Han

Iglesias

et al. 2022

Front. Physiol.

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“…In [12], the accuracy of word level classification for ASL is noted by varying the operating frequency ranging from 10 GHz to 77 GHz, which suggests that accuracy of identification tasks is higher with higher operating frequency. In [13], the authors made use of decision tree classifier to classify among 3 different human activities from the RF data. In [14], the authors have shown that the human pose can be estimated accurately through walls and occlusions using RF data.…”

Section: Introductionmentioning

confidence: 99%

RF-Sensing: A New Way to Observe Surroundings

et al. 2022

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Radio frequency sensing (RF-sensing) is an emerging field assisting vision technology for object detection, tracking, and various such use cases. RF-Sensing technology uses radio signals and their reflections in order to capture the surrounding details and then applies artificial intelligence algorithms to sense the objects. RF-sensing has advantage over the existing vision technology when it comes to low light conditions, privacy concerns, and far-range scenarios. In this paper, we propose different RFsensing methods for person identification (PI), human activity identification (HAI), and surroundinglocation identification (SLI). Here, we make use of data generated from millimeter-Wave sensors and preprocess it using signal processing techniques before feeding it to deep learning (DL) based classifiers. For PI and SLI, we propose a DL network consisting of convolutional neural network (CNN) and long short term memory (LSTM) blocks. Here, first the input is passed through the CNN layers and then followed by the LSTM. For HAI, we propose a hierarchical classifier consisting of two stages. First, similar activities are clubbed together and the coarse level classifier is trained to distinguish among the non-similar activities. This is followed by fine level classifiers, which are trained to distinguish between the those activities which are deemed to be similar while training the coarse level classifier. While testing, we employ the coarse level classifier unconditionally. The fine level classifier is employed only when the coarse level classifier's output class corresponds to that of similar activity. We use the output of the hierarchical classifier to determine the activity. Further, we show that the proposed algorithms for the PI and SLI are able to predict accurately among 5 persons and among 5 different surrounding-locations respectively and the proposed hierarchical classifier for HAI, which is used to classify among 11 different activities, shows an improvement of ≈ 4% compared to other DL based architectures in the literature.

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